Pythran as a Numpy backend

Using the flag —np-pythran, it is possible to use the Pythran numpyimplementation for numpy related operations. One advantage to use this backendis that the Pythran implementation uses C++ expression templates to save memorytransfers and can benefit from SIMD instructions of modern CPU.

This can lead to really interesting speedup in some cases, going from 2 up to16, depending on the targeted CPU architecture and the original algorithm.

Please note that this feature is experimental.

Usage example with setuptools

You first need to install Pythran. See its documentation for more information.

Then, simply add a cython: np_pythran=True directive at the top of thePython files that needs to be compiled using Pythran numpy support.

Here is an example of a simple setup.py file using setuptools:

from setuptools import setup
from Cython.Build import cythonize
 
setup(
    name = "My hello app",
    ext_modules = cythonize('hello_pythran.pyx')
)

Then, with the following header in hello_pythran.pyx:

# cython: np_pythran=True

hello_pythran.pyx will be compiled using Pythran numpy support.

Please note that Pythran can further be tweaked by adding settings in the$HOME/.pythranrc file. For instance, this can be used to enable Boost.SIMD support.See the Pythran user manual formore information.